Disease processes can affect any of the four chambers and four valves of a heart, altering their structure and/or function. Measuring the size, shape, and function of the chambers and valves provides useful information that can assist a physician in evaluating the effect of these disease processes, and of hemodynamic changes, and other influences on the heart. Such measurements may help in diagnosing a patient's health, evaluating the effect of a treatment, assessing prognosis, and understanding the underlying mechanisms of a disease process and its response to therapeutic interventions.
Most commonly, the left ventricle of the heart is investigated. The left ventricle is of greatest importance to health because it pumps blood through most of the body. The right ventricle is also studied, because it provides the impetus for blood circulation through the lungs. One of the most commonly used parameters of heart function is ejection fraction, which indicates the proportion of chamber volume ejected with each heart beat. Other important parameters include ventricular volume, the range of motion of the left ventricular or right ventricular wall, and detection of any thickening of the ventricular wall. These other parameters are generally indicators of regional function, and abnormalities in one or more of the parameters may indicate the presence of coronary artery disease. In addition, the shape of the ventricle provides information regarding its status, because the left ventricle becomes more spherical under certain loading conditions. An evaluation of all of these parameters--volume, shape, ejection fraction, wall motion, and wall thickening--can best be accomplished if the physician has an accurate representation of the ventricular contour.
In clinical practice, these cardiac parameters are generally not measured but rather are estimated visually from two-dimensional images. Methods have been developed to measure these parameters, but such methods typically employ geometrical assumptions concerning the shape of the ventricle. However, these assumptions limit the accuracy of the measurement methods because the heart is a complex organ, and in certain disease states, the geometric assumptions may be invalid. Recent studies have clearly shown that measurement of ventricular volume, mass, and infarct size are more accurate using a three-dimensional approach instead of a two-dimensional analysis. In three-dimensional imaging, the heart is imaged in multiple planes from which the contours of the heart are traced and used to reconstruct the endocardial (inner) and epicardial (outer) ventricular surfaces--without simplifying geometric assumptions. From the three-dimensional reconstruction of the heart, it is also possible to measure the ventricle's shape and parameters of regional function such as wall motion and wall thickening. Variability in measuring parameters of left ventricular size and function by three-dimensional echo is reduced compared to two-dimensional analysis because the latter requires measurements from specific orthogonal views, which cannot be precisely determined. Reducing variability is important to improve diagnostic sensitivity and specificity.
These advantages of quantitative three-dimensional echocardiography have many potential applications, including: diagnostic assessment of patients at rest, analysis of stress studies, pre-operative evaluation, monitoring of cardiac function during cardiac surgery, emergency room assessment of patients with acute chest pain and assessment of non-diagnostic electrocardiograms, evaluation of unstable patients in cardiac or surgical intensive care units, correlation of left ventricular function with perfusion and/or metabolism defects, assessment of prognosis, and quantitative comparison of serial studies to assess patient course, response to therapy, and left ventricular remodeling. Furthermore, an analysis of images throughout the cardiac cycle may make additional information on the synchrony of left ventricular contraction available for patient care; such information has been shown to improve diagnostic accuracy compared to analysis of end systolic function alone.
Manual tracing of ventricular contours from the multiple images of a patient's heart is tedious and time consuming, requiring some 12-15 hours per patient study. Quantitative analysis of such three-dimensional studies is thus impractical for routine patient care or in situations when time is of the essence. Furthermore, such image interpretation requires specialized training and experience on the part of the technician doing the tracing and is subject to variability and human error. Even two-dimensional image analysis methods are infrequently utilized in clinical practice, because hospital personnel lack the time to manually trace the ventricular contours in even just one or two imaging planes. It is therefore evident that application of these techniques for the quantitative analysis of three-dimensional echocardiographic images to patient care is not feasible unless contour delineation can be automated, so that results can be rapidly obtained.
Much effort has been expended over the past 15 years to develop an automated contour delineation algorithm for echocardiograms. The task is difficult because ultrasound images are inherently subject to noise, and the endocardial and epicardial contours comprise multiple tissue elements. Most of the research has been devoted to detection of contours from two-dimensional echo images. At first, attempts were made to trace the ventricular contour from static images. The earliest algorithms were gradient based edge detectors that searched among the gray scale values of the image pixels for a transition from light to dark, which might correspond to the border between the myocardium and the blood in a ventricular chamber. It was then necessary to identify those edge segments that should be strung together to form the ventricular contour. This task was typically performed by looking for local shape consistency and avoiding abrupt changes in contour direction. The edge detectors were usually designed to search radially from the center of the ventricle to locate the endocardial and epicardial contours. These prior art techniques were most applicable to short axis views. The application of an elliptical model enabled contour detection in apical views in which the left ventricle appears roughly elliptical in shape; however, the irregular contour in the region of the two valves at the basal end could not be accurately delineated. Another problem with some of the early edge detectors was that they traced all contours of the ventricular endocardium indiscriminately around and between the trabeculae carneae and papillary muscles. Subsequent methods were able to ignore these details of the musculature and to trace the smoother contour of the underlying endocardium.
Contour delineation accuracy improved when algorithms began to incorporate information available from tracking the motion of the heart as it contracts and expands with each beat during the cardiac cycle, instead of operating on a single static image. Indeed, human observers almost always utilize this type of temporal information when they trace contours manually. Similarities between temporally adjacent image frames are used to help fill in discontinuities or areas of signal dropout in an image, and to smooth the rough contours obtained using a radial search algorithm. The problems with these prior art methods are that: (a) the user generally has to manually trace the ventricular contour or identify a region of interest in the first image of the time series, (b) the errors at any frame in the series may be propagated to subsequent frames, and (c) the cardiac parameters of greatest clinical interest are derived from analysis of only two time points in the cardiac cycle--end diastole and end systole--and do not require frame by frame analysis.
Another way to utilize timing information is to measure the velocity of regional ventricular wall motion using optical flow techniques. However, wall motion and wall thickening are the parameters used clinically to evaluate cardiac status, not velocity. Also, such velocity measurements are very much subject to noise in the image, because the change in gray level from one image to the next may be caused by signal dropout or noise, rather than by the motion of the heart walls. The effect of this noise cannot be reduced by data smoothing over time because of the low frame rate (30 frames per second) relative to the heart rate (60-100 beats per minute).
The algorithm developed by Geiser et al. in "A Second-Generation Computer-Based Edge Detection Algorithm for Short Axis, Two-Dimensional Echocardiographic Images: Accuracy and Improvement in Interobserver Variability," J. Am. Soc. Echo 3:79-90 (1990) is much more accurate in contour delineation than those previously reported. The Geiser et al. algorithm incorporates not only temporal information, but also knowledge about the expected homogeneity of regional wall thickness by considering both the endocardial and epicardial contours. In addition, knowledge concerning the expected shape of the ventricular contour is applied to assist in connecting edge segments together into a contour. However, this method cannot be applied to three-dimensional echocardiograms, because the assumptions concerning ventricular shape are specific for standard two-dimensional imaging planes, such as the parasternal short axis view at mid ventricle, or the apical four chamber view. In a three-dimensional scan, the imaging planes may have a variety of locations and orientations in space. Another problem is that one of the assumptions used to select and connect edge segments--that the contour is elliptical--may not be valid under certain disease conditions in which the curvature of the interventricular septum is reversed.
Another way to use heart shape information is as a post processing step. As reported in "Automatic Contour Definition on left Ventriculograms by Image Evidence and a Multiple Template-Based Model," IEEE Trans. Med. Imag. 8:173-185 (1989), Lilly et al. used templates based on manually traced contours to verify the anatomical feasibility of the contours detected by their algorithm, and to make corrections to the contours. This method has only been used for contrast ventriculograms, however, and is probably not applicable to echocardiographic images.
Automated contour delineation algorithms for three-dimensional image sets at first merely extended the one and two-dimensional gradient based edge detectors to the spatial dimension. Some authors found edges in the individual two-dimensional images and then connected them into a three-dimensional surface. Others found edges based on three-dimensional gradients. However, as was seen in dealing with two-dimensional images, the problem is not to find gray scale edges, but rather to identify which of the many edges found in each image should be retained and connected to reconstruct the ventricular surface. A number of investigators have moved from connecting contour segments using simple shape models based on local smoothness criteria in space and time, to starting with a closed contour and deforming it to fit the image. An advantage of this approach is that the fitting procedure itself produces a surface reconstruction of the ventricle.
In their paper entitled, "Recovery of the 3-D Shape of the Left Ventricle from Echocardiographic Images," IEEE Trans. Med. Imag. 14:301-317 (1995), Coppini et al. explain how they employed a plastic surface that deforms to fit the gray scale information, to develop a three-dimensional shape. However their surface is basically a sphere pulled by springs, and cannot capture the complex anatomic shape of the ventricle with its outflow tract and valves. Other models have been developed, which are based on parametric functions, superquadratics, or finite element models, but these models require many terms to accurately represent complexities in ventricular shape, such as the sharp edges at the junction of the mitral valve annulus and the left ventricle, and the left ventricular outflow tract. This limitation is important because, although the global parameters of volume and mass are relatively insensitive to small localized errors, analysis of ventricular shape and regional function require accurate contour detection and reconstruction of the ventricular surface.
Another prior art approach is the active contour model or "snake," which deforms a closed contour under the influence of external forces to fit the gray scale features in an image. Three-dimensional active contour models have been developed; however, active contour models have limited accuracy due to their basis in geometric rather than anatomical models. They may produce anatomically implausible contours, because it is difficult to incorporate anatomical knowledge into the search for an accurate contour. Herlin et al. had to track the contour frame by frame through a cardiac cycle to prevent their active contour model from crossing into the left atrium when the mitral valve was open during diastole ("Performing Segmentation of Ultrasound Images Using Temporal Information," Proceedings 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York City, pp. 373-378). Individual segments of the contour can be constrained to specified shapes, but the model does not provide for any description of the interaction of the segments with each other to form an anatomically possible contour. Another problem is that for identification of complex structures such as the heart, the user may need to interact with the algorithm, to guide it or to initialize the fitting procedure. Finally, these models are difficult to apply to imaging planes that are randomly oriented in space, being more appropriate for images in parallel planes or images derived from rotational scans at fixed angular intervals.
One of the newer contour detection methods utilizes a knowledge based model of the ventricular contour called an active shape model. (See T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam, "Use of Active Shape Models for Locating Structures in Medical Images," which is included in Information Processing Medical Imaging, edited by H. H. Barrett and A. F. Gmitro, Berlin, Springer-Verlag, pp. 33-47, 1993.) Like active contour models, active shape models use an iterative refinement algorithm to search the image. The principal difference is that the active shape model can only be deformed in ways that are consistent with the statistical model derived from training data. This model of the shape of the ventricle is generated by performing a principal components analysis of the manually traced contours from a set of training images derived from ultrasound studies. The contours include a number of landmarks, which are consistently located, represent the same point in each study, and have gray scale characteristics that are determined from the training data. Automated contour detection is performed by searching the image for contour segments that match the landmarks in the model. This approach was developed for two-dimensional images acquired in standard imaging planes, but can also be applied to images in a single plane frame by frame through time, and through contiguous parallel planes in three-dimensional space when the change in the shape of the target organ between planes is small. However, no evidence has been provided to show that a three-dimensional model can be developed by this technique, or that the three-dimensional surface generated by this modeling technique accurately reconstructs the three-dimensional shape of the ventricle. Indeed, because this approach estimates the gray scale appearance of the target organ empirically, it cannot be applied to randomly oriented imaging planes.
Accordingly, it will be evident that there is a need for a new approach to automated contour delineation for three-dimensional reconstruction of cardiac structures from ultrasound scans, an approach that correctly identifies and delineates segments of the ventricular contours in a plurality of imaging planes, enabling an anatomically accurate reconstruction of the ventricular surface to be produced. The method used in this novel approach should not assume any fixed relationship between imaging planes, but instead should be applicable to images from any combination of imaging plane locations and orientations in space. In addition, the method should be applicable to reconstructing both the endocardial and epicardial contours, and to images acquired at any time point in the cardiac cycle.